{ "_id": "6a1f1d7fb401979e7341fbcf", "Package": "algebraic.mle", "Type": "Package", "Title": "Algebraic Maximum Likelihood Estimators", "Version": "2.0.2", "Authors@R": "person(\"Alexander\", \"Towell\", , \"lex@metafunctor.com\", role = c(\"aut\", \"cre\"), comment = c(ORCID = \"0000-0001-6443-9897\"))", "Description": "The maximum likelihood estimator (MLE) is a technology:\nunder regularity conditions, any MLE is asymptotically normal\nwith variance given by the inverse Fisher information. This\npackage exploits that structure by defining an algebra over\nMLEs. Compose independent estimators into joint MLEs via\nblock-diagonal covariance ('joint'), optimally combine repeated\nestimates via inverse-variance weighting ('combine'), propagate\ntransformations via the delta method ('rmap'), and bridge to\ndistribution algebra via conversion to normal or multivariate\nnormal objects ('as_dist'). Supports asymptotic ('mle',\n'mle_numerical') and bootstrap ('mle_boot') estimators with a\nunified interface for inference: confidence intervals, standard\nerrors, AIC, Fisher information, and predictive intervals. For\nbackground on maximum likelihood estimation, see Casella and\nBerger (2002, ISBN:978-0534243128). For the delta method and\nvariance estimation, see Lehmann and Casella (1998,\nISBN:978-0387985022).", "License": "GPL (>= 3)", "Encoding": "UTF-8", "ByteCompile": "true", "RoxygenNote": "7.3.3", "URL": "https://github.com/queelius/algebraic.mle,\nhttps://queelius.github.io/algebraic.mle/", "BugReports": "https://github.com/queelius/algebraic.mle/issues", "VignetteBuilder": "knitr", "Config/testthat/edition": "3", "Repository": "https://queelius.r-universe.dev", "Date/Publication": "2026-03-17 17:10:18 UTC", "RemoteUrl": "https://github.com/queelius/algebraic.mle", "RemoteRef": "HEAD", "RemoteSha": "d992f06a8602207597f58f82acfdcf32018c7fab", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-16 08:39:42 UTC", "User": "root" }, "Author": "Alexander Towell [aut, cre] (ORCID:\n)", "Maintainer": "Alexander Towell ", "MD5sum": "805bb54f14f618f3427175e6be858b9b", "_user": "queelius", "_type": "src", "_file": "algebraic.mle_2.0.2.tar.gz", "_fileid": "6d2aa1b298d5769996c311a9f625801bdf57644f681d2a79b87f11e6f8a35157", "_filesize": 914721, "_sha256": "6d2aa1b298d5769996c311a9f625801bdf57644f681d2a79b87f11e6f8a35157", "_created": "2026-05-16T08:39:42.000Z", "_published": "2026-06-02T18:14:23.713Z", "_distro": "noble", "_jobs": [ { "job": 79140679835, "time": 130, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "7032032827" }, { "job": 79140679747, "time": 124, "config": "linux-release-x86_64", "r": "4.6.0", "check": "OK", "artifact": "7032032165" }, { "job": 79140680197, "time": 93, "config": "macos-oldrel-arm64", "r": "4.5.3", "check": "OK", "artifact": "7032028656" }, { "job": 79140679756, "time": 89, "config": "macos-release-arm64", "r": "4.6.0", "check": "OK", "artifact": "7032028093" }, { "job": 79140679411, "time": 279, "config": "source", "r": "4.6.0", "check": "OK", "artifact": "7032017796" }, { "job": 79140678889, "time": 100, "config": "wasm-release", "r": "4.6.0", "check": "OK", "artifact": "7366139495" }, { "job": 79140680026, "time": 87, "config": "windows-devel", "r": "4.7.0", "check": "OK", "artifact": "7032028058" }, { "job": 79140680209, "time": 77, "config": "windows-oldrel", "r": "4.5.3", "check": "OK", "artifact": "7032026616" }, { "job": 79140680276, "time": 105, "config": "windows-release", "r": "4.6.0", "check": "OK", "artifact": "7032030019" } ], "_buildurl": "https://github.com/r-universe/queelius/actions/runs/25957452751", "_status": "success", "_host": "GitHub-Actions", "_upstream": "https://github.com/queelius/algebraic.mle", "_commit": { "id": "d992f06a8602207597f58f82acfdcf32018c7fab", "author": "Alex Towell ", "committer": "Alex Towell ", "message": "release: algebraic.mle v2.0.2 — code review fixes and ecosystem cleanup\n\n- Fixed pred() Monte Carlo integration (first sample used wrong params)\n- Fixed confint() ignoring parm argument\n- Fixed confint() NaN on negative variance (now informative error)\n- Fixed combine() parameter dimension validation\n- Removed re-export chain dependency (algebraic.dist MVN CDF fix)\n- Code simplification: extracted label_ci, block_diag, collect_or_null helpers\n- 248 tests, 0 failures\n\nCo-Authored-By: Claude Opus 4.6 (1M context) \n", "time": 1773767418 }, "_maintainer": { "name": "Alexander Towell", "email": "lex@metafunctor.com", "login": "queelius", "orcid": "0000-0001-6443-9897", "twitter": "@queelius", "uuid": 1896674 }, "_registered": true, "_dependencies": [ { "package": "algebraic.dist", "version": ">= 0.9.1", "role": "Imports" }, { "package": "stats", "role": "Imports" }, { "package": "boot", "role": "Imports" }, { "package": "mvtnorm", "role": "Imports" }, { "package": "MASS", "role": "Imports" }, { "package": "numDeriv", "role": "Imports" }, { "package": "testthat", "version": ">= 3.0.0", "role": "Suggests" }, { "package": "rmarkdown", "role": "Suggests" }, { "package": "knitr", "role": "Suggests" }, { "package": "ggplot2", "role": "Suggests" }, { "package": "tibble", "role": "Suggests" }, { "package": "CDFt", "role": "Suggests" } ], "_owner": "queelius", "_selfowned": true, "_usedby": 8, "_updates": [ { "week": "2025-49", "n": 3 }, { "week": "2025-50", "n": 1 }, { "week": "2025-51", "n": 2 }, { "week": "2026-06", "n": 2 }, { "week": "2026-07", "n": 1 }, { "week": "2026-09", "n": 10 }, { "week": "2026-10", "n": 1 }, { "week": "2026-11", "n": 11 }, { "week": "2026-12", "n": 1 } ], "_tags": [ { "name": "v1.0.0", "date": "2026-02-02" }, { "name": "v1.1.0", "date": "2026-02-13" } ], "_stars": 1, "_contributors": [ { "user": "queelius", "count": 154, "uuid": 1896674 } ], "_userbio": { "uuid": 1896674, "type": "user", "name": "Alex Towell", "description": "Alexander Towell (Alex Towell). Reliability theory, encrypted search, algebraic structures. Building personal data tools." }, "_downloads": { "count": 600, "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/algebraic.mle" }, "_devurl": "https://github.com/queelius/algebraic.mle", "_pkgdown": "https://queelius.github.io/algebraic.mle/", "_searchresults": 73, "_rbuild": "4.6.0", "_assets": [ "extra/algebraic.mle.html", "extra/citation.cff", "extra/citation.html", "extra/citation.json", "extra/citation.txt", "extra/contents.json", "extra/NEWS.html", "extra/NEWS.txt", "extra/readme.html", "extra/readme.md", "manual.pdf" ], "_homeurl": "https://github.com/queelius/algebraic.mle", "_realowner": "queelius", "_cranurl": true, "_releases": [ { "version": "0.9.0", "date": "2026-01-09" }, { "version": "2.0.2", "date": "2026-03-19" } ], "_exports": [ "as_dist", "bias", "cdf", "combine", "conditional", "confint_from_sigma", "expectation", "inv_cdf", "is_mle", "is_mle_boot", "joint", "marginal", "mle", "mle_boot", "mle_numerical", "mse", "nparams", "obs", "observed_fim", "orthogonal", "params", "pred", "rmap", "sampler", "score_val", "se", "sup" ], "_help": [ { "page": "as_dist.mle_fit", "title": "Convert an MLE to a distribution object.", "topics": [ "as_dist.mle_fit" ] }, { "page": "as_dist.mle_fit_boot", "title": "Convert a bootstrap MLE to an empirical distribution.", "topics": [ "as_dist.mle_fit_boot" ] }, { "page": "bias", "title": "Generic method for computing the bias of an estimator object.", "topics": [ "bias" ] }, { "page": "bias.mle_fit", "title": "Computes the bias of an `mle_fit` object assuming the large sample approximation is valid and the MLE regularity conditions are satisfied. In this case, the bias is zero (or zero vector).", "topics": [ "bias.mle_fit" ] }, { "page": "bias.mle_fit_boot", "title": "Computes the estimate of the bias of a `mle_fit_boot` object.", "topics": [ "bias.mle_fit_boot" ] }, { "page": "cdf.mle_fit", "title": "CDF of the asymptotic distribution of an MLE.", "topics": [ "cdf.mle_fit" ] }, { "page": "coef.mle_fit", "title": "Extract coefficients from an 'mle_fit' object.", "topics": [ "coef.mle_fit" ] }, { "page": "combine", "title": "Combine independent MLEs for the same parameter.", "topics": [ "combine", "combine.list", "combine.mle_fit" ] }, { "page": "conditional.mle_fit", "title": "Conditional distribution from an MLE.", "topics": [ "conditional.mle_fit" ] }, { "page": "confint_from_sigma", "title": "Function to compute the confidence intervals from a variance-covariance matrix", "topics": [ "confint_from_sigma" ] }, { "page": "confint.mle_fit", "title": "Function to compute the confidence intervals of `mle_fit` objects.", "topics": [ "confint.mle_fit" ] }, { "page": "confint.mle_fit_boot", "title": "Method for obtained the confidence interval of an `mle_fit_boot` object. Note: This impelements the `vcov` method defined in `stats`.", "topics": [ "confint.mle_fit_boot" ] }, { "page": "density.mle_fit", "title": "PDF of the asymptotic distribution of an MLE.", "topics": [ "density.mle_fit" ] }, { "page": "density.mle_fit_boot", "title": "PDF of the empirical distribution of bootstrap replicates.", "topics": [ "density.mle_fit_boot" ] }, { "page": "dim.mle_fit", "title": "Dimension (number of parameters) of an MLE.", "topics": [ "dim.mle_fit" ] }, { "page": "dim.mle_fit_boot", "title": "Dimension (number of parameters) of a bootstrap MLE.", "topics": [ "dim.mle_fit_boot" ] }, { "page": "expectation.mle_fit", "title": "Expectation operator applied to `x` of type `mle_fit` with respect to a function `g`. That is, `E(g(x))`.", "topics": [ "expectation.mle_fit" ] }, { "page": "inv_cdf.mle_fit", "title": "Quantile function of the asymptotic distribution of an MLE.", "topics": [ "inv_cdf.mle_fit" ] }, { "page": "is_mle", "title": "Determine if an object `x` is an `mle_fit` object.", "topics": [ "is_mle" ] }, { "page": "is_mle_boot", "title": "Determine if an object is an `mle_fit_boot` object.", "topics": [ "is_mle_boot" ] }, { "page": "joint", "title": "Compose independent MLEs into a joint MLE.", "topics": [ "joint", "joint.mle_fit" ] }, { "page": "logLik.mle_fit", "title": "Log-likelihood of an 'mle_fit' object.", "topics": [ "logLik.mle_fit" ] }, { "page": "marginal.mle_fit", "title": "Method for obtaining the marginal distribution of an MLE that is based on asymptotic assumptions:", "topics": [ "marginal.mle_fit" ] }, { "page": "mean.mle_fit", "title": "Mean of the asymptotic distribution of an MLE.", "topics": [ "mean.mle_fit" ] }, { "page": "mean.mle_fit_boot", "title": "Mean of 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for obtaining the number of observations in the sample used by an `mle_fit`.", "topics": [ "nobs.mle_fit" ] }, { "page": "nobs.mle_fit_boot", "title": "Method for obtaining the number of observations in the sample used by an `mle_fit_boot`.", "topics": [ "nobs.mle_fit_boot" ] }, { "page": "nparams.mle_fit", "title": "Method for obtaining the number of parameters of an `mle_fit` object.", "topics": [ "nparams.mle_fit" ] }, { "page": "nparams.mle_fit_boot", "title": "Method for obtaining the number of parameters of an `boot` object.", "topics": [ "nparams.mle_fit_boot" ] }, { "page": "obs.mle_fit", "title": "Method for obtaining the observations used by the `mle_fit` object `x`.", "topics": [ "obs.mle_fit" ] }, { "page": "obs.mle_fit_boot", "title": "Method for obtaining the observations used by the `mle_fit_boot`.", "topics": [ "obs.mle_fit_boot" ] }, { "page": "observed_fim", "title": "Generic method for computing the observed FIM of an `mle_fit` object.", "topics": [ "observed_fim" ] 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